On Variational Inference for User Modeling in Attribute-Driven Collaborative Filtering

TL;DR

This paper introduces a variational inference approach for user modeling in attribute-driven collaborative filtering, leveraging causal inference and temporal contexts to improve attribute prediction in recommender systems.

Contribution

It presents a novel probabilistic model that incorporates causal inference and temporal data, advancing user-attribute affinity estimation in recommender systems.

Findings

Outperforms standard baseline methods on real-world datasets

Effective modeling of user-attribute affinities using causal inference

Improved next attribute prediction accuracy

Abstract

Recommender Systems have become an integral part of online e-Commerce platforms, driving customer engagement and revenue. Most popular recommender systems attempt to learn from users' past engagement data to understand behavioral traits of users and use that to predict future behavior. In this work, we present an approach to use causal inference to learn user-attribute affinities through temporal contexts. We formulate this objective as a Probabilistic Machine Learning problem and apply a variational inference based method to estimate the model parameters. We demonstrate the performance of the proposed method on the next attribute prediction task on two real world datasets and show that it outperforms standard baseline methods.